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Abstract：
The rapid, low cost, and efficient detection of SARS-CoV-2 virus infection, especially in clinical samples, remains a major challenge. A promising solution to this problem is the combination of a spectroscopic technique: surface-enhanced Raman spectroscopy (SERS) with advanced chemometrics based on machine learning (ML) algorithms. In the present study, we conducted SERS investigations of saliva and nasopharyngeal swabs taken from a cohort of patients (saliva: 175; nasopharyngeal swabs: 114). Obtained SERS spectra were analyzed using a range of classifiers in which random forest (RF) achieved the best results, e.g., for saliva, the precision and recall equals 94.0% and 88.9%, respectively. The results demonstrate that even with a relatively small number of clinical samples, the combination of SERS and shallow machine learning can be used to identify SARS-CoV-2 virus in clinical practice.
摘要：
快速，低成本,并有效检测SARS-CoV-2病毒感染，尤其是在临床样本中，仍然是一个重大挑战。解决此问题的有希望的解决方案是光谱技术的结合：表面增强拉曼光谱（SERS）与基于机器学习（ML）算法的高级化学计量学。在本研究中,我们对一组患者的唾液和鼻咽拭子进行了SERS检查(唾液:175;鼻咽拭子:114).获得的SERS光谱使用一系列分类器进行分析，其中随机森林（RF）取得了最好的结果，例如，唾液，准确率和召回率分别为94.0%和88.9%，分别。结果表明，即使临床样本数量相对较少，SERS和浅层机器学习的结合可用于在临床实践中识别SARS-CoV-2病毒。
